Abstract
Purchase PDF (2833 K)
E-mail Article
Add to my Quick Links
Cited By in Scopus (0)
Purchase PDF (526 K)
doi:10.1016/j.future.2007.05.006 
Copyright © 2007 Elsevier Ltd All rights reserved.
Received 18 April 2006;
References and further reading may be available for this article. To view references and further reading you must purchase this article.
Abstract
Research interest in Grid computing has grown significantly over the past five years. Management of distributed resources is one of the key issues in Grid computing. Central to management of resources is the effectiveness of resource allocation as it determines the overall utility of the system. The current approaches to brokering in a Grid environment are non-coordinated since application-level schedulers or brokers make scheduling decisions independently of the others in the system. Clearly, this can exacerbate the load sharing and utilization problems of distributed resources due to sub-optimal schedules that are likely to occur. To overcome these limitations, we propose a mechanism for coordinated sharing of distributed clusters based on computational economy. The resulting environment, called Grid-Federation, allows the transparent use of resources from the federation when local resources are insufficient to meet its users’ requirements. The use of computational economy methodology in coordinating resource allocation not only facilitates the Quality of Service (QoS)-based scheduling, but also enhances utility delivered by resources. We show by simulation, while some users that are local to popular resources can experience higher cost and/or longer delays, the overall users’ QoS demands across the federation are better met. Also, the federation’s average case message-passing complexity is seen to be scalable, though some jobs in the system may lead to large numbers of messages before being scheduled.
Article Outline
- 1. Introduction
- 1.1. Grid-Federation
- 2. Related work
- 3. Grid-Federation: Architecture for decentralized resource management
- 3.1. Decentralised market place and Grid-Federation
- 3.2. General Grid-Federation superscheduling technique
- 3.3. QoS driven resource allocation algorithm for Grid-Federation
- 3.4. Quote value
- 3.5. User budget and deadline
- 4. Experiments and analysis
- 4.1. Workload and resource methodology
- 4.2. Experiment 1 — independent resources
- 4.3. Experiment 2 — with federation
- 4.4. Experiment 3 — with federation and economy
- 4.5. Experiment 4 — message complexity with respect to jobs
- 4.6. Experiment 5 — message complexity with respect to system size
- 4.7. Results and observations
- 5. Conclusion
- Acknowledgements
- References
- Vitae
